Make APIs, databases and background work hold up when demand rises. The engagement connects investigation to implementation, with a baseline, reviewed changes and clear acceptance criteria.
What’s included.
Three steps.
Understand
Agree the task, baseline, source systems and delivery boundary.
Build
Develop and review the code, configuration and integrations in scope.
Verify
Test representative cases, compare with the baseline and hand over.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01Slow-database workload remediation
What you may be seeing
- Our reports take minutes to open.
- Checkout sometimes waits for the database.
- The app slows down when several staff use it together.
- We upgraded the database, but the same queries are still slow.
- Orders or updates occasionally time out or block each other.
We repair expensive database queries and the application code that uses them, so routine work stays responsive when more people use the system.
Implementation & verification
We identify expensive query patterns using query statistics, lock information and execution plans on representative data. We rewrite wasteful SQL (the language used to query a database) and application data access, remove repeated per-item queries and add only indexes supported by the workload. We shorten blocking transactions and tune connection pooling, query limits or caching without returning stale transactional results. We compare execution time, database load and concurrency behavior before deploying a reversible schema or code change.
How we verify the work
- Rank query families by total time and business impact, not just one unusually slow request.
- Check scanned rows, joins, index selectivity, N+1 (repeating a database request for every item) access and lock waits separately.
- Use safe representative testing; EXPLAIN ANALYZE executes the query, so isolate write statements.
- Accept on lower p95 (the time 95% of requests fall below) request time and lock waits under load, with identical business results.
Tools considered for this work
Delivery: SQL (the language used to query a database)/backend development + database-native tooling. PostgreSQL: pg stat statements, EXPLAIN _ _ plans, lock views and PgBouncer. Alternatives: MySQL slow-query log/EXPLAIN or SQL (the language used to query a database) Server Query Store. Observability: existing APM or OpenTelemetry/Grafana. Verification: representative SQL (the language used to query a database) fixtures and k6 API (a controlled connection between applications) load scripts.
02Peak-demand capacity engineering
What you may be seeing
- Our website crashes during promotions.
- Everything works until lots of buyers arrive together.
- We lose orders when traffic suddenly spikes.
- Background jobs pile up during busy periods.
- We keep spare servers running because we fear another outage.
We test realistic busy periods, remove the limiting bottleneck and plan how the system should recover when demand exceeds capacity.
Implementation & verification
We reproduce the real browsing-to-purchase traffic mix and identify whether the bottleneck is an API (a controlled connection between applications), database, queue or upstream service. We change code and infrastructure to remove contention, queue noncritical work and cache eligible read traffic. We configure autoscaling and bounded retries around measured limits, preserving checkout correctness when dependencies slow down. We validate sustained load, sudden spikes and recovery behavior before setting a capacity plan tied to business demand.
How we verify the work
- Test ramp, spike and soak traffic with realistic think time and shared-resource contention.
- Separate throughput saturation from slow dependencies; remove the limiting dependency before adding replicas.
- Bound queues/retries and test overload behavior without issuing real customer charges.
- Set acceptance thresholds for successful orders, p95 (the time 95% of requests fall below)/p99 (the time 99% of requests fall below) latency, errors and recovery time.
Tools considered for this work
Delivery: backend development + infrastructure configuration + load testing. Load: Grafana k6 or Locust; realistic account/cart fixtures. Infrastructure: existing AWS/Azure/GCP stack; Kubernetes HPA where applicable. Queues/cache: SQS or RabbitMQ; Redis; CDN (a network that delivers content closer to visitors) for eligible content. Monitoring: APM, database metrics and queue age dashboards.
03Serverless response-time remediation
What you may be seeing
- The first request is very slow after the app has been quiet.
- Some users wait much longer than everyone else.
- Serverless functions time out when traffic rises.
- Our functions spend too long starting or opening connections.
- Increasing function memory raised costs without a clear improvement.
We reduce the wait caused by starting or connecting small cloud functions, then check that reliability and running costs still meet your needs.
Implementation & verification
We separate cold-start, initialization, execution and downstream waiting time in serverless request traces. We reduce startup work and package weight, reuse permitted clients/connections and parallelize independent calls. We benchmark memory and concurrency settings, then consider provisioned capacity only when its cost fits the latency requirement. We verify warm and cold request distributions, dependency timeouts and cost per successful invocation before rollout.
How we verify the work
- Break down the slowest percentile by initialization, compute, network and database connection time.
- Remove eager work and serialize only calls with real dependencies; constrain connection counts.
- Benchmark a memory/concurrency matrix and compare provisioned capacity against its ongoing cost.
- Accept on p95 (the time 95% of requests fall below)/p99 (the time 99% of requests fall below) response time, timeout rate and invocation cost under realistic burst traffic.
Tools considered for this work
Delivery: function/backend development + serverless configuration. AWS: Lambda, CloudWatch, tracing and Lambda Power Tuning. Alternatives: Azure Functions or Google Cloud Run with native request/startup telemetry. Deployment: existing SAM/CDK/Terraform or function pipeline. Testing: k6 and controlled cold/warm request replay.
Find the limiting factor.
We connect request timings, database activity and queue behaviour. Automated load tests reproduce the pressure; AI-assisted analysis can help group recurring failures for engineering review.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Speed
Loading, interaction and slow-request response times.
Reliability
Successful transactions and recovery under realistic load.
Unit cost
Spend per completed action, with traffic and quality considered.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
Is this more than a website speed audit?
Yes. We work across the frontend, backend, database, cloud and AI workload. The diagnosis determines which layers need development; the engagement can include implementation and ongoing monitoring.
Can you improve our Azure environment?
We can assess Azure application performance, workload costs, Functions, database usage and monitoring. We agree the relevant services, access and implementation boundaries before work starts.
Will you guarantee a savings percentage?
No. We establish your baseline first and present evidence-backed opportunities. Any saving is checked against reliability, customer experience and output quality.